1. Field of the Invention
Embodiments of the present invention generally relate to dynamic adjustment of noise filter strengths for use with dynamic range enhancement of images.
2. Description of the Related Art
Imaging and video capabilities have become the trend in consumer electronics. Digital cameras, digital camcorders, and video cellular phones are common, and many other new gadgets are evolving in the market. Advances in large resolution CCD/CMOS sensors coupled with the availability of low-power digital signal processors (DSPs) has led to the development of digital cameras with both high resolution image and short audio/visual clip capabilities. The high resolution (e.g., a sensor with a 2560×1920 pixel array) provides quality offered by traditional film cameras.
As the camera sensor and signal processing technologies advanced, the nominal performance indicators of camera performance, e.g., picture size, zooming, and range, reached saturation in the market. Because of this, end users shifted their focus back to actual or perceivable picture quality. The criteria users use in judging picture quality may include signal to noise ratio (SNR) (especially in dark regions), blur due to hand shake, blur due to fast moving objects, natural tone, natural color, etc.
The perceived quality of still images and video is heavily influenced by how the brightness/contrast of a scene is rendered, which makes brightness/contrast enhancement (BCE) one of the fundamental parts of an image pipeline. BCE is a challenging problem because human perception of brightness/contrast is quite complex and is highly dependent on the content of a still image or video frames. Many current BCE methods do not adequately address this complexity. When tested on large sets of images, these methods may fail in certain scenes (e.g., flat objects, clouds in a sky) because image content is very diverse. That is, many current BCE methods apply a fixed technique to all images/frames regardless of content and, as a result, may produce poor quality results on some images/frames because they do not adapt to content variation.
Research efforts in tone related issues have been focused on contrast enhancement (CE), which is further classified into global CE and local CE. More particularly, techniques for global CE and local CE may be realized by global histogram equalization (global HE or HE) and local histogram equalization (local HE or LHE), respectively. The histogram of an image, i.e., the pixel value distribution of an image, represents the relative frequency of occurrence of gray levels within the image. Histogram modification techniques modify an image so that its histogram has a desired shape. This is useful in stretching the low-contrast levels of an image with a narrow histogram. Global histogram equalization is designed to re-map input gray levels into output gray levels so that the output image has flat occurrence probability (i.e., a uniform probability density function) at each gray level, thereby achieving contrast enhancement. The use of global HE can provide better detail in photographs that are over or under-exposed. However, such plain histogram equalization cannot always be directly applied because the resulting output image is excessively enhanced (over-enhancement) or insufficiently enhanced (under-enhancement).
Local histogram equalization (LHE) may be applied to alleviate some of the issues of global HE. In general, LHE enhances details over small areas (i.e., areas whose total pixel contribution to the total number of image pixels has a negligible influence on the global transform), which adjusts contrast on the basis of a local neighborhood, e.g., a block or sub-region, instead of the entire image. This approach helps with the under-enhancement issue.
Tests have shown that applying both global and local contrast enhancement outperforms the use of global contrast enhancement alone in almost all cases.